# Load in the whole Alzheimers data file
data <- read.csv('~/repos/portfolio/demonstrative/R/datasets/alzheimers/alzheimers.csv')
# Remove this field ... I forgot to do that when creating the dataset
data <- data %>% select(-male)
# Normalize gender labels
stopifnot(all(!is.na(data$gender)))
normalize.gender <- function(x) {
  is.male <- x %>% tolower %>% str_detect('^m')
  ifelse(is.male, 'Male', 'Female') %>% factor
}
data <- data %>% mutate(gender=normalize.gender(gender))
# Convert integer fields to numeric for the sake of consistency
data <- data %>% mutate_each_(funs(as.numeric), c('Betacellulin', 'Eotaxin_3'))
head(data)
X <- data %>% select(-response)
y <- data[,'response']
table(y)
y
   Impaired NotImpaired 
         91         242 
table(y) / length(y)
y
   Impaired NotImpaired 
  0.2732733   0.7267267 
names(X)
  [1] "ACE_CD143_Angiotensin_Converti"   "ACTH_Adrenocorticotropic_Hormon" 
  [3] "AXL"                              "Adiponectin"                     
  [5] "Alpha_1_Antichymotrypsin"         "Alpha_1_Antitrypsin"             
  [7] "Alpha_1_Microglobulin"            "Alpha_2_Macroglobulin"           
  [9] "Angiopoietin_2_ANG_2"             "Angiotensinogen"                 
 [11] "Apolipoprotein_A_IV"              "Apolipoprotein_A1"               
 [13] "Apolipoprotein_A2"                "Apolipoprotein_B"                
 [15] "Apolipoprotein_CI"                "Apolipoprotein_CIII"             
 [17] "Apolipoprotein_D"                 "Apolipoprotein_E"                
 [19] "Apolipoprotein_H"                 "B_Lymphocyte_Chemoattractant_BL" 
 [21] "BMP_6"                            "Beta_2_Microglobulin"            
 [23] "Betacellulin"                     "C_Reactive_Protein"              
 [25] "CD40"                             "CD5L"                            
 [27] "Calbindin"                        "Calcitonin"                      
 [29] "CgA"                              "Clusterin_Apo_J"                 
 [31] "Complement_3"                     "Complement_Factor_H"             
 [33] "Connective_Tissue_Growth_Factor"  "Cortisol"                        
 [35] "Creatine_Kinase_MB"               "Cystatin_C"                      
 [37] "EGF_R"                            "EN_RAGE"                         
 [39] "ENA_78"                           "Eotaxin_3"                       
 [41] "FAS"                              "FSH_Follicle_Stimulation_Hormon" 
 [43] "Fas_Ligand"                       "Fatty_Acid_Binding_Protein"      
 [45] "Ferritin"                         "Fetuin_A"                        
 [47] "Fibrinogen"                       "GRO_alpha"                       
 [49] "Gamma_Interferon_induced_Monokin" "Glutathione_S_Transferase_alpha" 
 [51] "HB_EGF"                           "HCC_4"                           
 [53] "Hepatocyte_Growth_Factor_HGF"     "I_309"                           
 [55] "ICAM_1"                           "IGF_BP_2"                        
 [57] "IL_11"                            "IL_13"                           
 [59] "IL_16"                            "IL_17E"                          
 [61] "IL_1alpha"                        "IL_3"                            
 [63] "IL_4"                             "IL_5"                            
 [65] "IL_6"                             "IL_6_Receptor"                   
 [67] "IL_7"                             "IL_8"                            
 [69] "IP_10_Inducible_Protein_10"       "IgA"                             
 [71] "Insulin"                          "Kidney_Injury_Molecule_1_KIM_1"  
 [73] "LOX_1"                            "Leptin"                          
 [75] "Lipoprotein_a"                    "MCP_1"                           
 [77] "MCP_2"                            "MIF"                             
 [79] "MIP_1alpha"                       "MIP_1beta"                       
 [81] "MMP_2"                            "MMP_3"                           
 [83] "MMP10"                            "MMP7"                            
 [85] "Myoglobin"                        "NT_proBNP"                       
 [87] "NrCAM"                            "Osteopontin"                     
 [89] "PAI_1"                            "PAPP_A"                          
 [91] "PLGF"                             "PYY"                             
 [93] "Pancreatic_polypeptide"           "Prolactin"                       
 [95] "Prostatic_Acid_Phosphatase"       "Protein_S"                       
 [97] "Pulmonary_and_Activation_Regulat" "RANTES"                          
 [99] "Resistin"                         "S100b"                           
[101] "SGOT"                             "SHBG"                            
[103] "SOD"                              "Serum_Amyloid_P"                 
[105] "Sortilin"                         "Stem_Cell_Factor"                
[107] "TGF_alpha"                        "TIMP_1"                          
[109] "TNF_RII"                          "TRAIL_R3"                        
[111] "TTR_prealbumin"                   "Tamm_Horsfall_Protein_THP"       
[113] "Thrombomodulin"                   "Thrombopoietin"                  
[115] "Thymus_Expressed_Chemokine_TECK"  "Thyroid_Stimulating_Hormone"     
[117] "Thyroxine_Binding_Globulin"       "Tissue_Factor"                   
[119] "Transferrin"                      "Trefoil_Factor_3_TFF3"           
[121] "VCAM_1"                           "VEGF"                            
[123] "Vitronectin"                      "von_Willebrand_Factor"           
[125] "age"                              "tau"                             
[127] "p_tau"                            "Ab_42"                           
[129] "Genotype"                         "gender"                          
X %>% sapply(class) %>% table
.
 factor numeric 
      2     128 
#X %>% sapply(class) %>% .[. == 'factor']
names(X)[sapply(X, class) == 'factor']
[1] "Genotype" "gender"  

Partial Analysis

We could start by looking at the relationship between some of the more intuitive variables like Age, Gender, and Genotype and impairment, to see if there are any obvious relationships.

Age vs Impairment

data %>% 
  mutate(age_range=cut(age, breaks=5)) %>%
  group_by(age_range, response) %>% tally %>% 
  plot_ly(x=~age_range, y=~n, color=~response, type='bar') %>%
  plotly::layout(hovermode='closest', title='Age vs Impairment')
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels

Genotype vs Impairment

data %>% 
  group_by(Genotype, response) %>% tally %>% 
  plot_ly(x=~Genotype, y=~n, color=~response, type='bar') %>%
  plotly::layout(hovermode='closest', title='Genotype vs Impairment')
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels

Gender vs Impairment

data %>% 
  group_by(gender, response) %>% tally %>% 
  plot_ly(x=~gender, y=~n, color=~response, type='bar') %>%
  layout(hovermode='closest', title='Gender vs Impairment')
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels

Protein Analysis

Now what about the other ~125 variables? We can’t look at them all one at a time so perhaps there is a way to “condense” them into something more manageable:

library(corrplot)
d.pca <- X %>% select(-gender, -Genotype)
cor.mat <- corrplot(cor(d.pca), order='hclust', tl.col='black', tl.cex=.5)

# Extract the variable names from the figure below since they will be useful
# for creating similar plots for comparison (and it's easier to compare in the same order)
cor.var <- rownames(cor.mat)
#d.pca <- X %>% select(-gender, -Genotype)
pca <- prcomp(d.pca, scale=T)
data.frame(Cumulative.Variance=summary(pca)$importance[3,]) %>% mutate(PC.Number=1:n()) %>%
  plot_ly(x=~PC.Number, y=~Cumulative.Variance, mode='lines', type='scatter') %>%
  layout(title='Cumulative Variance Explained by Principal Components')
corrplot(cor(d.pca[,cor.var], pca$x[,1:25]), tl.col='black', tl.cex=.5)

Regression Models

#registerCores(1)
tr <- proj$getTrainer()
tc <- trainControl(
  classProbs=T, method='cv', number=10,
  summaryFunction = function(...)c(twoClassSummary(...), defaultSummary(...)),
  verboseIter=F, savePredictions='final', returnResamp='final', allowParallel=T
)
get.ensemble.model <- function(tuneList){
  caret.list.args <- list(
    trControl=trainControl(
      method='cv', number=5, classProbs=T, 
      returnData=F, savePredictions='final',
      allowParallel=T, verboseIter=F
    ),
    tuneList=tuneList
  )
  caret.stack.args <- list(
    method=GetEnsembleAveragingModel(),
    trControl=trainControl(method='none', savePredictions = 'final')
  )
  GetCaretEnsembleModel(caret.list.args, caret.stack.args)
}
X.m <- predict(dummyVars(~., X), X) %>% as.data.frame
non.pca.vars <- X.m %>% select(starts_with('gender'), starts_with('Genotype'), starts_with('age')) %>% names
pca.split <- function(X) {
  list(
    var=X %>% dplyr::select(one_of(non.pca.vars)),
    pca=X %>% dplyr::select(-one_of(non.pca.vars))
  )
}
pca.combine <- function(pp, X) {
  X <- pca.split(X)
  cbind(X$var, predict(pp, newdata=X$pca))
}
get.model <- function(model.name){
  model <- getModelInfo()[[model.name]]
  m <- model
  
  m$fit <- function(x, y, wts, param, lev, last, classProbs, ...){
    X <- pca.split(x)
    pp <- preProcess(X$pca, method=c('center', 'scale', 'pca'), pcaComp = 40)
    X.train <- cbind(X$var, predict(pp, newdata=X$pca))
    modelFit <- model$fit(X.train, y, wts, param, lev, last, classProbs, ...)
    modelFit$pp <- pp
    modelFit$feature.names <- names(X.train)
    modelFit
  }
  m$predict <- function (modelFit, newdata, submodels = NULL) {
    X.test <- pca.combine(modelFit$pp, newdata)
    model$predict(modelFit, X.test, submodels)
  }
  m$prob <- function (modelFit, newdata, submodels = NULL){
    X.test <- pca.combine(modelFit$pp, newdata)
    model$prob(modelFit, X.test, submodels)
  }
  if (model.name == 'xgbTree'){
    m$varImp = function(object, numTrees = NULL, ...) {
      imp <- xgb.importance(object$feature.names, model = object)
      imp <- as.data.frame(imp)[, 1:2]
      rownames(imp) <- as.character(imp[,1])
      imp <- imp[,2,drop = FALSE]
      colnames(imp) <- "Overall"
      imp   
    }
  }
  m
}
models <- list(
  tr$getModel('pca_glm', method=get.model('glm'), trControl=tc),
  tr$getModel('pca_glmnet', method=get.model('glmnet'), trControl=tc, tuneLength=5),
  tr$getModel('pca_rf', method=get.model('rf'), trControl=tc, tuneGrid=expand.grid(.mtry = c(2,4,8))),
  tr$getModel('pca_rpart', method=get.model('rpart'), tuneLength=10, trControl=tc),
  tr$getModel('pca_gbm', method=get.model('gbm'), tuneLength=5, trControl=tc, verbose=F),
  tr$getModel('pca_xgb', method=get.model('xgbTree'), tuneLength=5, trControl=tc),
  tr$getModel('pca_spline', method=get.model('earth'), preProcess=pre.proc, trControl=tc, tuneLength=5),
  tr$getModel('pca_nnet', method=get.model('nnet'), preProcess=pre.proc, trControl=tc, tuneLength=5, trace=F)
)
names(models) <- sapply(models, function(m) m$name)
pca.results <- lapply(models, function(m) tr$train(m, X.m, y, enable.cache=T)) %>% setNames(names(models))
2016-11-07 18:36:04 INFO::Beginning training for model "pca_glm" (cache name = "model_pca_glm")
2016-11-07 18:36:04 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_pca_glm.Rdata" from disk
2016-11-07 18:36:04 INFO::Training complete for model "pca_glm"
2016-11-07 18:36:04 INFO::Beginning training for model "pca_glmnet" (cache name = "model_pca_glmnet")
2016-11-07 18:36:04 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_pca_glmnet.Rdata" from disk
2016-11-07 18:36:04 INFO::Training complete for model "pca_glmnet"
2016-11-07 18:36:04 INFO::Beginning training for model "pca_rf" (cache name = "model_pca_rf")
2016-11-07 18:36:04 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_pca_rf.Rdata" from disk
2016-11-07 18:36:04 INFO::Training complete for model "pca_rf"
2016-11-07 18:36:04 INFO::Beginning training for model "pca_rpart" (cache name = "model_pca_rpart")
2016-11-07 18:36:04 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_pca_rpart.Rdata" from disk
2016-11-07 18:36:04 INFO::Training complete for model "pca_rpart"
2016-11-07 18:36:04 INFO::Beginning training for model "pca_gbm" (cache name = "model_pca_gbm")
2016-11-07 18:36:04 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_pca_gbm.Rdata" from disk
2016-11-07 18:36:04 INFO::Training complete for model "pca_gbm"
2016-11-07 18:36:04 INFO::Beginning training for model "pca_xgb" (cache name = "model_pca_xgb")
2016-11-07 18:36:04 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_pca_xgb.Rdata" from disk
2016-11-07 18:36:04 INFO::Training complete for model "pca_xgb"
2016-11-07 18:36:04 INFO::Beginning training for model "pca_spline" (cache name = "model_pca_spline")
2016-11-07 18:36:04 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_pca_spline.Rdata" from disk
2016-11-07 18:36:04 INFO::Training complete for model "pca_spline"
2016-11-07 18:36:04 INFO::Beginning training for model "pca_nnet" (cache name = "model_pca_nnet")
2016-11-07 18:36:04 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_pca_nnet.Rdata" from disk
2016-11-07 18:36:04 INFO::Training complete for model "pca_nnet"
library(caretEnsemble)
pre.proc <- c('center', 'scale')
ens.model <- list(
  glmnet=caretModelSpec(method='glmnet', preProcess=pre.proc, tuneLength=5),
  gbm=caretModelSpec(method='gbm', tuneLength=5, verbose=F),
  spline=caretModelSpec(method='earth', tuneLength=5, preProcess=pre.proc)
)
models <- list(
  tr$getModel('glm', method='glm', preProcess=pre.proc, trControl=tc),
  tr$getModel('glmnet', method='glmnet', preProcess=pre.proc, trControl=tc, tuneLength=5),
  tr$getModel('nnet', method='nnet', preProcess=pre.proc, trControl=tc, tuneLength=5, trace=F),
  tr$getModel('rpart', method='rpart', tuneLength=10, trControl=tc),
  tr$getModel('gbm', method='gbm', tuneLength=5, trControl=tc, verbose=F),
  tr$getModel('xgb', method='xgbTree', tuneLength=5, trControl=tc),
  tr$getModel('spline', method='earth', preProcess=pre.proc, trControl=tc, tuneLength=5),
  tr$getModel('rf', method='rf', trControl=tc, tuneLength=5),
  tr$getModel('ensemble', method=get.ensemble.model(ens.model), trControl=tc, tuneLength=5)
)
names(models) <- sapply(models, function(m) m$name)
all.results <- lapply(models, function(m) tr$train(m, X.m, y, enable.cache=T)) %>% setNames(names(models))
2016-11-07 20:37:59 INFO::Beginning training for model "glm" (cache name = "model_glm")
2016-11-07 20:37:59 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_glm.Rdata" from disk
2016-11-07 20:37:59 INFO::Training complete for model "glm"
2016-11-07 20:37:59 INFO::Beginning training for model "glmnet" (cache name = "model_glmnet")
2016-11-07 20:37:59 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_glmnet.Rdata" from disk
2016-11-07 20:37:59 INFO::Training complete for model "glmnet"
2016-11-07 20:37:59 INFO::Beginning training for model "nnet" (cache name = "model_nnet")
2016-11-07 20:37:59 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_nnet.Rdata" from disk
2016-11-07 20:37:59 INFO::Training complete for model "nnet"
2016-11-07 20:37:59 INFO::Beginning training for model "rpart" (cache name = "model_rpart")
2016-11-07 20:37:59 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_rpart.Rdata" from disk
2016-11-07 20:37:59 INFO::Training complete for model "rpart"
2016-11-07 20:37:59 INFO::Beginning training for model "gbm" (cache name = "model_gbm")
2016-11-07 20:37:59 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_gbm.Rdata" from disk
2016-11-07 20:37:59 INFO::Training complete for model "gbm"
2016-11-07 20:37:59 INFO::Beginning training for model "xgb" (cache name = "model_xgb")
2016-11-07 20:37:59 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_xgb.Rdata" from disk
2016-11-07 20:37:59 INFO::Training complete for model "xgb"
2016-11-07 20:37:59 INFO::Beginning training for model "spline" (cache name = "model_spline")
2016-11-07 20:37:59 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_spline.Rdata" from disk
2016-11-07 20:37:59 INFO::Training complete for model "spline"
2016-11-07 20:37:59 INFO::Beginning training for model "rf" (cache name = "model_rf")
2016-11-07 20:37:59 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_rf.Rdata" from disk
2016-11-07 20:37:59 INFO::Training complete for model "rf"
2016-11-07 20:37:59 INFO::Beginning training for model "ensemble" (cache name = "model_ensemble")
2016-11-07 20:37:59 DEBUG::Restoring cached object "~/data/meetups/r_tutorials/tutorial_03/cache/models/model_ensemble.Rdata" from disk
2016-11-07 20:37:59 INFO::Training complete for model "ensemble"
rbind(GetResampleData(pca.results), GetResampleData(all.results)) %>%
  mutate(model=reorder(model, accuracy, median)) %>%
  plot_ly(x=~model, y=~accuracy, type='box') %>%
  layout(margin=list(b=100))
var.imp <- GetVarImp(all.results)
var.imp %>%
  mutate(feature=reorder(var.imp$feature, var.imp$score, mean)) %>%
  plot_ly(x=~feature, y=~score, color=~model, mode='markers', type='scatter') %>%
  layout(margin=list(b=200))
var.model.names <- names(pca.results)[!str_detect(names(pca.results), 'nnet')]
var.imp <- GetVarImp(pca.results[var.model.names])
var.imp %>%
  mutate(feature=reorder(var.imp$feature, var.imp$score, mean)) %>%
  plot_ly(x=~feature, y=~score, color=~model, mode='markers', type='scatter') %>%
  layout(margin=list(b=200))
pd.vars <- c('tau', 'p_tau', 'Ab_42', 'age', 'gender.Male', 'gender.Female')
pd.models <- c('xgb', 'gbm', 'glmnet', 'spline', 'ensemble')
#pd.models <- c('ensemble')
pred.fun <- function(object, newdata) {
  if ('caretStack' %in% class(object$finalModel)){
    pred <- predict(object$finalModel, newdata=newdata, type='prob')
  } else {
    pred <- predict(object, newdata=newdata, type='prob')
  }
  if (is.vector(pred)) pred
  else pred[,1] 
}
options(error=recover)
#registerCores(1) # Increase this to make PD calcs faster
pd.data <- GetPartialDependence(
  all.results[pd.models], pd.vars, pred.fun, 
  X=X.m, # This can come from model objects but only if returnData=T in trainControl
  grid.size=50, grid.window=c(0, 1), # Resize these to better fit range of data
  sample.rate=1, # Decrease this if PD calculations take too long
  verbose=F, seed=SEED
)
pd.mean <- foreach(pd=pd.data, .combine=rbind)%do%
  { pd$pd %>% dplyr::mutate(predictor=pd$predictor, model=pd$model) } %>%
  dplyr::group_by(predictor, model, x) %>% 
  dplyr::summarise(y.mid=mean(y)) %>% ungroup 
pd.mean %>% ggplot(aes(x=x, y=y.mid, color=model)) + geom_line() + facet_wrap(~predictor, scale='free') +
  theme_bw() + 
  ylab('Predicted Probability') + xlab('Predictor Value')

glmnet.model <- all.results$glmnet$fit$finalModel
glmnet.coef <- predict(glmnet.model, s=all.results$glmnet$fit$bestTune$lambda, type='coefficients')
glmnet.coef <- glmnet.coef[,1]
glmnet.coef %>% data.frame %>% setNames('Coefficient') %>% add_rownames(var='Feature') %>%
  filter(abs(Coefficient) > 0) %>%
  mutate(Feature=reorder(Feature, Coefficient, mean)) %>%
  plot_ly(x=~Feature, y=~Coefficient, type='bar') %>%
  layout(margin=list(b=200), title='Glmnet Coefficients')
---
title: "R Notebook"
output: html_notebook
---


```{r init, results='hide', warning=F, message=F, error=F, echo=F}
source('common.R')
library(ggplot2)
library(corrplot)
library(stringr)
library(reshape2)
library(plotly)
library(knitr)
layout <- plotly::layout

# Paper on data:
# Multiplexed Immunoassay Panel Identifies Novel CSF Biomarkers for Alzheimer's Disease Diagnosis and Prognosis
# library(AppliedPredictiveModeling)
# ?diagnosis
```

```{r}
# Load in the whole Alzheimers data file
data <- read.csv('~/repos/portfolio/demonstrative/R/datasets/alzheimers/alzheimers.csv')

# Remove this field ... I forgot to do that when creating the dataset
data <- data %>% select(-male)

# Normalize gender labels
stopifnot(all(!is.na(data$gender)))
normalize.gender <- function(x) {
  is.male <- x %>% tolower %>% str_detect('^m')
  ifelse(is.male, 'Male', 'Female') %>% factor
}
data <- data %>% mutate(gender=normalize.gender(gender))

# Convert integer fields to numeric for the sake of consistency
data <- data %>% mutate_each_(funs(as.numeric), c('Betacellulin', 'Eotaxin_3'))

head(data)
```


```{r}
X <- data %>% select(-response)
y <- data[,'response']
table(y)
```

```{r}
table(y) / length(y)
```

```{r}
names(X)
```

```{r}
# Base R to accomplish the same:
# table(sapply(X, class))

X %>% sapply(class) %>% table
```

```{r}
# Pipeline to accomplish the same:
# X %>% sapply(class) %>% .[. == 'factor']

names(X)[sapply(X, class) == 'factor']
```

# Partial Analysis

We could start by looking at the relationship between some of the more intuitive variables like Age, Gender, and Genotype and impairment, to see if there are any obvious relationships.

## Age vs Impairment

```{r}
data %>% 
  mutate(age_range=cut(age, breaks=5)) %>%
  group_by(age_range, response) %>% tally %>% 
  plot_ly(x=~age_range, y=~n, color=~response, type='bar') %>%
  plotly::layout(hovermode='closest', title='Age vs Impairment')
```


## Genotype vs Impairment

```{r}
data %>% 
  group_by(Genotype, response) %>% tally %>% 
  plot_ly(x=~Genotype, y=~n, color=~response, type='bar') %>%
  plotly::layout(hovermode='closest', title='Genotype vs Impairment')
```


## Gender vs Impairment

```{r}
data %>% 
  group_by(gender, response) %>% tally %>% 
  plot_ly(x=~gender, y=~n, color=~response, type='bar') %>%
  layout(hovermode='closest', title='Gender vs Impairment')
```


### Protein Analysis

Now what about the other ~125 variables?  We can't look at them all one at a time so perhaps there is a way to "condense" them into something more manageable:


```{r, fig.width=10, fig.height=10}
library(corrplot)
d.pca <- X %>% select(-gender, -Genotype)
cor.mat <- corrplot(cor(d.pca), order='hclust', tl.col='black', tl.cex=.5)

# Extract the variable names from the figure below since they will be useful
# for creating similar plots for comparison (and it's easier to compare in the same order)
cor.var <- rownames(cor.mat)
```


```{r}
#d.pca <- X %>% select(-gender, -Genotype)
pca <- prcomp(d.pca, scale=T)
```




```{r}
data.frame(Cumulative.Variance=summary(pca)$importance[3,]) %>% mutate(PC.Number=1:n()) %>%
  plot_ly(x=~PC.Number, y=~Cumulative.Variance, mode='lines', type='scatter') %>%
  layout(title='Cumulative Variance Explained by Principal Components')
```


```{r, fig.width=4, fig.height=10}
corrplot(cor(d.pca[,cor.var], pca$x[,1:25]), tl.col='black', tl.cex=.5)
```


# Regression Models

```{r}
#registerCores(1)


tr <- proj$getTrainer()
tc <- trainControl(
  classProbs=T, method='cv', number=10,
  summaryFunction = function(...)c(twoClassSummary(...), defaultSummary(...)),
  verboseIter=F, savePredictions='final', returnResamp='final', allowParallel=T
)

get.ensemble.model <- function(tuneList){
  caret.list.args <- list(
    trControl=trainControl(
      method='cv', number=5, classProbs=T, 
      returnData=F, savePredictions='final',
      allowParallel=T, verboseIter=F
    ),
    tuneList=tuneList
  )
  caret.stack.args <- list(
    method=GetEnsembleAveragingModel(),
    trControl=trainControl(method='none', savePredictions = 'final')
  )
  GetCaretEnsembleModel(caret.list.args, caret.stack.args)
}

X.m <- predict(dummyVars(~., X), X) %>% as.data.frame

non.pca.vars <- X.m %>% select(starts_with('gender'), starts_with('Genotype'), starts_with('age')) %>% names

pca.split <- function(X) {
  list(
    var=X %>% dplyr::select(one_of(non.pca.vars)),
    pca=X %>% dplyr::select(-one_of(non.pca.vars))
  )
}

pca.combine <- function(pp, X) {
  X <- pca.split(X)
  cbind(X$var, predict(pp, newdata=X$pca))
}

get.model <- function(model.name){
  model <- getModelInfo()[[model.name]]
  m <- model
  
  m$fit <- function(x, y, wts, param, lev, last, classProbs, ...){
    X <- pca.split(x)
    pp <- preProcess(X$pca, method=c('center', 'scale', 'pca'), pcaComp = 40)
    X.train <- cbind(X$var, predict(pp, newdata=X$pca))
    modelFit <- model$fit(X.train, y, wts, param, lev, last, classProbs, ...)
    modelFit$pp <- pp
    modelFit$feature.names <- names(X.train)
    modelFit
  }
  m$predict <- function (modelFit, newdata, submodels = NULL) {
    X.test <- pca.combine(modelFit$pp, newdata)
    model$predict(modelFit, X.test, submodels)
  }
  m$prob <- function (modelFit, newdata, submodels = NULL){
    X.test <- pca.combine(modelFit$pp, newdata)
    model$prob(modelFit, X.test, submodels)
  }
  if (model.name == 'xgbTree'){
    m$varImp = function(object, numTrees = NULL, ...) {
      imp <- xgb.importance(object$feature.names, model = object)
      imp <- as.data.frame(imp)[, 1:2]
      rownames(imp) <- as.character(imp[,1])
      imp <- imp[,2,drop = FALSE]
      colnames(imp) <- "Overall"
      imp   
    }
  }
  m
}
```


```{r}
models <- list(
  tr$getModel('pca_glm', method=get.model('glm'), trControl=tc),
  tr$getModel('pca_glmnet', method=get.model('glmnet'), trControl=tc, tuneLength=5),
  tr$getModel('pca_rf', method=get.model('rf'), trControl=tc, tuneGrid=expand.grid(.mtry = c(2,4,8))),
  tr$getModel('pca_rpart', method=get.model('rpart'), tuneLength=10, trControl=tc),
  tr$getModel('pca_gbm', method=get.model('gbm'), tuneLength=5, trControl=tc, verbose=F),
  tr$getModel('pca_xgb', method=get.model('xgbTree'), tuneLength=5, trControl=tc),
  tr$getModel('pca_spline', method=get.model('earth'), preProcess=pre.proc, trControl=tc, tuneLength=5),
  tr$getModel('pca_nnet', method=get.model('nnet'), preProcess=pre.proc, trControl=tc, tuneLength=5, trace=F)
)
names(models) <- sapply(models, function(m) m$name)

pca.results <- lapply(models, function(m) tr$train(m, X.m, y, enable.cache=T)) %>% setNames(names(models))
```

```{r}
library(caretEnsemble)
pre.proc <- c('center', 'scale')

ens.model <- list(
  glmnet=caretModelSpec(method='glmnet', preProcess=pre.proc, tuneLength=5),
  gbm=caretModelSpec(method='gbm', tuneLength=5, verbose=F),
  spline=caretModelSpec(method='earth', tuneLength=5, preProcess=pre.proc)
)
models <- list(
  tr$getModel('glm', method='glm', preProcess=pre.proc, trControl=tc),
  tr$getModel('glmnet', method='glmnet', preProcess=pre.proc, trControl=tc, tuneLength=5),
  tr$getModel('nnet', method='nnet', preProcess=pre.proc, trControl=tc, tuneLength=5, trace=F),
  tr$getModel('rpart', method='rpart', tuneLength=10, trControl=tc),
  tr$getModel('gbm', method='gbm', tuneLength=5, trControl=tc, verbose=F),
  tr$getModel('xgb', method='xgbTree', tuneLength=5, trControl=tc),
  tr$getModel('spline', method='earth', preProcess=pre.proc, trControl=tc, tuneLength=5),
  tr$getModel('rf', method='rf', trControl=tc, tuneLength=5),
  tr$getModel('ensemble', method=get.ensemble.model(ens.model), trControl=tc, tuneLength=5)
)
names(models) <- sapply(models, function(m) m$name)

all.results <- lapply(models, function(m) tr$train(m, X.m, y, enable.cache=T)) %>% setNames(names(models))
```


```{r}
rbind(GetResampleData(pca.results), GetResampleData(all.results)) %>%
  mutate(model=reorder(model, accuracy, median)) %>%
  plot_ly(x=~model, y=~accuracy, type='box') %>%
  layout(margin=list(b=100))
```


```{r}
var.imp <- GetVarImp(all.results)
var.imp %>%
  mutate(feature=reorder(var.imp$feature, var.imp$score, mean)) %>%
  plot_ly(x=~feature, y=~score, color=~model, mode='markers', type='scatter') %>%
  layout(margin=list(b=200))
```



```{r}
var.model.names <- names(pca.results)[!str_detect(names(pca.results), 'nnet')]
var.imp <- GetVarImp(pca.results[var.model.names])
var.imp %>%
  mutate(feature=reorder(var.imp$feature, var.imp$score, mean)) %>%
  plot_ly(x=~feature, y=~score, color=~model, mode='markers', type='scatter') %>%
  layout(margin=list(b=200))
```


<!-- {r} -->
<!-- #pc3 <- results$pca_xgb$fit$finalModel$pp$rotation[,3]  -->
<!-- scale_vec <- function(x) (x - median(x)) / IQR(x) -->
<!-- results$pca_xgb$fit$finalModel$pp$rotation %>% t %>% apply(2, scale_vec) %>%  -->
<!--   as.data.frame %>% add_rownames(var='PC') %>%  -->
<!--   plot_ly(x=~tau, y=~Ab_42, text=~PC, type='scatter', mode='markers') -->


```{r}

pd.vars <- c('tau', 'p_tau', 'Ab_42', 'age', 'gender.Male', 'gender.Female')
pd.models <- c('xgb', 'gbm', 'glmnet', 'spline', 'ensemble')
#pd.models <- c('ensemble')

pred.fun <- function(object, newdata) {
  if ('caretStack' %in% class(object$finalModel)){
    pred <- predict(object$finalModel, newdata=newdata, type='prob')
  } else {
    pred <- predict(object, newdata=newdata, type='prob')
  }
  if (is.vector(pred)) pred
  else pred[,1] 
}
options(error=recover)
#registerCores(1) # Increase this to make PD calcs faster
pd.data <- GetPartialDependence(
  all.results[pd.models], pd.vars, pred.fun, 
  X=X.m, # This can come from model objects but only if returnData=T in trainControl
  grid.size=50, grid.window=c(0, 1), # Resize these to better fit range of data
  sample.rate=1, # Decrease this if PD calculations take too long
  verbose=F, seed=SEED
)

```


```{r, fig.width=6, fig.height=3}
pd.mean <- foreach(pd=pd.data, .combine=rbind)%do%
  { pd$pd %>% dplyr::mutate(predictor=pd$predictor, model=pd$model) } %>%
  dplyr::group_by(predictor, model, x) %>% 
  dplyr::summarise(y.mid=mean(y)) %>% ungroup 

pd.mean %>% ggplot(aes(x=x, y=y.mid, color=model)) + geom_line() + facet_wrap(~predictor, scale='free') +
  theme_bw() + 
  ylab('Predicted Probability') + xlab('Predictor Value')
```


```{r}
glmnet.model <- all.results$glmnet$fit$finalModel
glmnet.coef <- predict(glmnet.model, s=all.results$glmnet$fit$bestTune$lambda, type='coefficients')
glmnet.coef <- glmnet.coef[,1]
glmnet.coef %>% data.frame %>% setNames('Coefficient') %>% add_rownames(var='Feature') %>%
  filter(abs(Coefficient) > 0) %>%
  mutate(Feature=reorder(Feature, Coefficient, mean)) %>%
  plot_ly(x=~Feature, y=~Coefficient, type='bar') %>%
  layout(margin=list(b=200), title='Glmnet Coefficients')
```

<!-- Graveyard -->


<!-- # The code below will plot the PCA loadings (ie pca$rotation) matrix -->
<!-- # directly rather than looking at correlations between original and transformed variables -->
<!-- # (though these show about the same thing) -->

<!--
{r, fig.height=10, fig.width=6}
library(reshape2)
dp <- pca$rotation[,1:25][cor.var,] %>% as.data.frame %>%
  add_rownames(var='feature') %>%
  melt(id.vars='feature', variable.name='pc')
dp$feature <- factor(dp$feature, levels=rev(cor.var))
dp %>% ggplot(aes(x=pc, y=feature, fill=value)) + geom_tile() +
  scale_fill_gradient2(low='red', high='blue', mid='white')
-->

<!-- PCA + Data Projections -->
<!-- {r, fig.width=10, fig.height=10} -->
<!-- i.pca <- c(1,2) -->
<!-- d.pca.pred <- as.data.frame(predict(pca, d.pca)[,i.pca]) %>% setNames(., c('PC1', 'PC2')) -->
<!-- d.pca.pred$response <- y -->

<!-- # d.pca.pred %>% plot_ly(x=~PC1, y=~PC2, color=~response, type='scatter', mode='markers') -->

<!-- # Parameters for axis with no grid lines, ticks or labels -->
<!-- empty.axis <- list( -->
<!--   title = '', -->
<!--   zeroline = FALSE, -->
<!--   showline = FALSE, -->
<!--   showticklabels = FALSE, -->
<!--   ticklen = 0, -->
<!--   showgrid = FALSE -->
<!-- ) -->

<!-- # Create a line plot of each variable showing which direction it moves within our 2D space -->
<!-- p1 <- plot_ly( -->
<!--     d.pca.pred, x=~PC2, y=~PC1, type='scatter', color=~response,  -->
<!--     mode='markers', opacity=1 -->
<!--   ) %>%  -->
<!--   layout( -->
<!--     xaxis=list(showgrid=F, zeroline=T), -->
<!--     yaxis=list(showgrid=F, zeroline=T) -->
<!--   ) -->

<!-- # Create a heatmap of impairment incidence rate across our 2D space -->
<!-- d.hm <- d.pca.pred %>%  -->
<!--   mutate(PC1=as.character(cut(PC1, breaks=3)), PC2=as.character(cut(PC2, breaks=3))) %>% -->
<!--   group_by(PC1, PC2) %>% summarise(PCT=100*sum(response == 'Impaired')/n()) %>% -->
<!--   acast(PC1 ~ PC2, value.var='PCT') -->
<!-- p2 <- plot_ly(z=d.hm[c(3,2,1),], type='heatmap', reversescale=F) #%>% -->
<!--   #layout(xaxis=empty.axis, yaxis=empty.axis) -->

<!-- # Overlay the above plots on top of one another -->
<!-- subplot(p2, p1, margin=-1) %>%  -->
<!--   layout( -->
<!--     paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',  -->
<!--     width=750, height=500, -->
<!--     title='2D Projection of Correlated Features Overlayed w/ Impairment Rates' -->
<!--   ) -->



<!-- TSNE projections -->
<!-- {r} -->
<!-- library(tsne) -->
<!-- d.tsne <- X %>% select(-gender, -Genotype) -->
<!-- scale_vec <- function(x) (x - mean(x)) / sd(x) -->
<!-- d.tsne <- d.tsne %>% mutate_each(funs(scale_vec)) -->
<!-- m.tsne <- tsne(d.tsne) -->
<!-- m.tsne %>% as.data.frame %>% ggplot(aes(x=V1, y=V2)) + geom_point() -->

